1. Field of the Invention
Implementations of various technologies described herein generally relate to techniques for modeling geological properties of the earth and, more particularly, to techniques for generating facies probability cubes that can be used with multipoint statistics to create a reservoir model.
2. Description of the Related Art
The following descriptions and examples are not admitted to be prior art by virtue of their inclusion within this section.
Geological modeling and reservoir characterization provide quantitative 3D reservoir models based on available reservoir measurements, such as well log interpretations, experimental results from core analysis, seismic survey and dynamic fluid flow responses from field observations (e.g., historic production data) and pressure change data. One type of reservoir characterization or modeling technique is stochastic reservoir modeling.
Stochastic reservoir modeling has gained popularity in modern reservoir modeling software because of its ability to constrain its model based on a variety of reservoir data and its computational efficiency in generating complex reservoir models with millions of voxels. Stochastic reservoir modeling also allows users to quantitatively evaluate uncertainties in the model due to the lack of knowledge of the reservoirs. The data used to constrain the reservoir models in stochastic reservoir modeling are primarily classified into two categories: “hard data” or “soft data.” Hard data includes data such as those measured in wells (e.g., well log data), which are considered to be accurate information and should be honored during simulations. Soft data are not as accurate as hard data but typically have larger or better coverage of the reservoir. Facies probability cubes are considered to be soft data that have been derived from seismic attributes using well to seismic calibrations. These types of soft data are important in guiding inter-well facies prediction and thus, they may be used to reduce uncertainties in the decision making process for reservoir management.
Geostatistics provide variety of algorithms and tools to build stochastic reservoir models. Generally, there are two approaches for using geostatistics to build stochastic reservoir models: a pixel-based approach and an object-based approach. The pixel-based approach proceeds by gridding the reservoir into pixels (voxels) and simulating each pixel (voxel) in a random sequence. Unlike the pixel-based approach, the object-based approach directly drops the facies (geobody) objects into the simulated reservoir according to the specified geometric information of these geobodies. The pixel-based approach provides increased flexibility for constraining the model according to reservoir data but it often has poor shape reproduction in the final reservoir models. In contrast, the object-based approach tends to generate more realistic shapes of geobodies; however, it becomes more difficult to constrain the models according to local reservoir observations, particularly when there are dense well locations.
In the pixel-based approach, a sequential indicator simulation (SIS) is often used to create facies models. However, a newly emerging pixel-based approach named Multi-point statistics (MPS) is gaining more attention from modelers and is considered to be part of an advanced facies modeling suite. MPS uses 1D, 2D or 3D “training images” as quantitative templates to model subsurface property fields. MPS modeling captures geological structures from training images and anchors them to data locations. As such, MPS takes advantage of a 2-point (variogram-based) geostatistical approach and an object-based approach to create flexibility in data conditioning while producing more realistic shapes from the training images. MPS can then integrate soft data, such as a facies probability cube, to generate geological or reservoir models. The resulting geological or reservoir models can then be used for oil field explorations by identifying hydrocarbon deposits in the Earth.
In addition to being used in multipoint statistics facies modeling, probability cubes are also used for other modeling approaches, such as object-based modeling and pixel-based Sequential Indicator Simulation (SIS), to further constrain the simulated earth models. As such, facies probability cubes play an important role in geological modeling or reservoir characterization. In particular, facies probability cubes can assist in geological modeling or reservoir characterization when well data is scarce. However, automatically generating facies probability cubes that are geologically sound remains a challenge.